Interpretive Summary: Food Safety Inspection Service (FSIS) established requirements for the zero tolerance for fecal contaminated carcasses entering the chiller. The manual organoleptic process FSIS currently uses to inspect carcasses has not improved much, and even the current zero tolerance inspection is only required for 10 birds twice per shift per line. Thus, most processing plants inspect only 0.5% of all birds processed per day for fecal contaminants. The ARS real-time multispectral imaging system has demonstrated a science-based tool for fecal detection during poultry processing. The common aperture camera based imaging system was able to inspect every bird in a real-time mode. However, due to time constraints for real-time, on-line applications and limited applicable image processing algorithms, moderate false positives were detected. To improve system performance by minimizing false positive errors, several image processing methods were tested at commercial poultry processing industry. In this paper, the test results of real-time, on-line multispectral imaging system for fecal detection at commercial poultry processing plants were reported.

Technical Abstract:
Previous research has demonstrated the ARS real-time multispectral imaging system was effective as a tool for fecal detection during poultry processing. The imaging system with two-band (517 and 565 nm) common aperture camera was able to inspect every bird in real-time, online mode. With several image processing methods including binning, cuticle removal filter, median filter, and morphological analysis in real-time mode, the system performance was improved by maximizing detection accuracy and minimizing false positive errors. Most false positives were caused by wing bone, thigh, vent and boundary, respectively. Although more experiments need to be conducted to confirm the detection accuracy, because the number of true positives confirmed by inspector were only 14 out of 29,821 carcasses (0.05%), the ARS multispectral imaging system was able to detect contaminants with promising detection accuracy (approximately 91%) and 3.3% false positive errors at the processing speed of 150 birds per minute from the two-day in-plant trials.